1. Technical Field
Statistical models of appearance are widely used in computer vision and have many applications, including interpreting medical images and interpreting images containing faces.
2. Related Art
Conventionally, a statistical model is built which represents intensity (greyscale or colour) variation across an image or part of an image. In the case of a facial appearance model, images of many faces are used to generate the model, these images being known as training images. The intensity variation that is likely to be seen in any given face will tend to include similar patterns, and the statistical model represents these patterns. Once the statistical model has been built it may be used to determine whether a new image includes a face, by determining whether the new image contains the facial intensity patterns. The identity of the face in the image may be determined by comparing the intensity patterns found in the image with identity patterns of known faces. A positive identification is given if a suitable error measure, such as a sum of squared error metric, is below a predetermined threshold value.
There are several known methods of generating statistical appearance models, and using the models to identify and/or recognize faces or other objects in images. Two known models, the Active Shape Model (ASM) [3,4] and the Active Appearance Model (AAM) [2] were developed at the Victoria University of Marichester, United Kingdom and have been widely used. Both of these models are based upon the use of normalized intensity values. The ASM and the AAM are both generalizations of eigen-face models [6]. Eigen-face models are also based upon the use of intensity values and have also been widely used.
A disadvantage of statistical appearance models that are based upon intensity variation information is that these models are prone to function incorrectly in the presence of changing lighting effects. For example, a face illuminated from an angle different to the illumination which was used when generating the model may cause an error.
It is Known to generate an ASM using images to which a non-linear filter has been applied. One such filter is arranged to locate edges in a given image, and then set to zero the intensity of everything that is not an edge (this is known as the Canny Edge Operator). The same filter is applied to a new image when the ASM is used to identify and/or recognise an object in the image the output of the filter is a pair of images, one of which represents the directions of edges of the image, and the other represents the magnitude of the edges. This method suffers from two disadvantages. The first disadvantage is that the resulting images are binary images, each edge being represented by ‘on’ values, with the result that a significant amount of information relating to the image is lost. In particular, information relating to structure close to the edges is lost. The second disadvantage of the method is that the filter parameters remain the same for every area of each image, and remain the same for every image. Although it is possible to adjust the filter parameters, tile method does not provide any measurement upon which to base any adjustment.